Abstract | ||
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A biometrics system is to find out the identity of a person by measuring physical and physiological features which can distinguish
the corresponding person from others. When applying the conventional machine learning methods to design a biometrics system,
one first runs into the difficulty of collecting sufficient data for each person to be registered to the system. In addition,
there can be almost infinite number of variations of non-registered data. Therefore, it is very difficult to analyze and predict
the distributional properties of data that are essential for the system to process real data in practical applications. These
difficulties require a new framework of identification and verification, which is appropriate and efficient for the special
situations of biometrics systems. As a preliminary solution, the present paper proposes a simple but theoretically well-defined
method based on the statistical test theory.
|
Year | DOI | Venue |
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2002 | 10.1007/3-540-45683-X_81 | PRICAI |
Keywords | Field | DocType |
verification method,statistical identification,statistical test,machine learning | Pattern recognition,Computer science,Artificial intelligence,Biometrics,Special situation,Rejection rate,Machine learning,Statistical hypothesis testing | Conference |
ISBN | Citations | PageRank |
3-540-44038-0 | 0 | 0.34 |
References | Authors | |
1 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kwanyong Lee | 1 | 13 | 4.38 |
Hyeyoung Park | 2 | 194 | 32.70 |